SMS Spam Filtering: Methods and Data

Mobile or SMS spam is a real and growing problem primarily due to the availability of very cheap bulk pre-pay SMS packages and the fact that SMS engenders higher response rates as it is a trusted and personal service. SMS spam filtering is a relatively new task which inherits many issues and solu- tions from email spam filtering. However it poses its own specific challenges. This paper motivates work on filtering SMS spam and reviews recent devel- opments in SMS spam filtering. The paper also discusses the issues with data collection and availability for furthering research in this area, analyses a large corpus of SMS spam, and provides some initial benchmark results

A Review on mobile SMS Spam filtering techniques

Under short messaging service (SMS) spam is understood the unsolicited or undesired messages received on mobile phones. These SMS spams constitute a veritable nuisance to the mobile subscribers. This marketing practice also worries service providers in view of the fact that it upsets their clients or even causes them lose subscribers. By way of mitigating this practice, researchers have proposed several solutions for the detection and filtering of SMS spams. In this paper, we present a review of the currently available methods, challenges, and future research directions on spam detection techniques, filtering, and mitigation of mobile SMS spams. The existing research literature is critically reviewed and analyzed. The most popular techniques for SMS spam detection, filtering, and mitigation are compared, including the used data sets, their findings, and limitations, and the future research directions are discussed. This review is designed to assist expert researchers to identify open areas that need further improvement

Pembangunan elemen kemahiran hijau dalam pengajaran dan pembelajaran (PdP) bagi pensyarah kolej vokasional

Kemahiran hijau (Green Skill) merupakan satu kemahiran berasaskan kepandaian dan kecekapan yang menjadi aset kepada setiap individu sebelum menerokai semua bidang pekerjaan ke arah pembangunan yang mampan. Kajian kualitatif ini menggunakan kaedah penerokaan sebagai reka bentuk kajian yang bertujuan untuk membangunkan elemen kemahiran hijau dalam pengajaran dan pembelajaran (PdP) bagi pensyarah kolej vokasional. Pada fasa pertama iaitu fasa pembangunan, pengkaji telah menjalankan temu bual bersama tiga (3) orang pensyarah yang mempunyai kepakaran di dalam bidang PdP dan Teknologi Pembinaan. Selepas melaksanakan protokol temu bual maklumat telah ditemakan melalui analisis tematik dan seterusnya telah dianalisis melalui analisis matrik bersama semakan literatur sistematik bagi mendapatkan persamaan dan perbezaan maklumat. Pada fasa kedua iaitu fasa pengesahan, seramai lima (5) orang pakar telah membuat pengesahan terhadap format dan kandungan itemitem yang telah dikeluarkan. Fasa ini melibatkan dua belas (12) orang pakar yang terdiri daripada pensyarah yang mempunyai pengalaman selama sepuluh (10) tahun dan ke atas sebagai responden utama. Melalui teknik Fuzzy Delphi sebagai prosedur penganalisian data, Data kajian telah di analisis bagi mendapatkan nilai purata m 1 (nilai minimum), m 2 (nilai paling munasabah) dan m 3 (nilai maksimum), seterusnya nilai ‘d’ Threshold value, konsensus 75% pengesahan kumpulan pakar dan Fuzzy Evaluation. Di dalam kajian ini, hanya satu (1) item iaitu “mengguna kertas terpakai untuk sebarang tugasan” daripada elemen kemahiran hijau dalam penilaian dan tugasan telah ditolak kerana nilai d≤0.2 iaitu 0.243 dan peratus kesepakatan tidak mencapai >75% namun peratusan keseluruhan konstuk bagi elemen tersebut diterima dengan jumlah sebanyak 97.92% dan nilai d= 0.126. Seterusnya, item-item yang lain untuk keseluruhan elemen telah diterima oleh pihak kumpulan pakar bagi meneruskan kajian. Kesimpulanya, elemen kemahiran hijau dalam PdP perlu dilanjutkan sebagai garis panduan di dalam PdP untuk para pendidik pada masa akan datang

A discrete hidden Markov model for SMS spam detection

Many machine learning methods have been applied for short messaging service (SMS) spam detection, including traditional methods such as naive Bayes (NB), vector space model (VSM), and support vector machine (SVM), and novel methods such as long short-term memory (LSTM) and the convolutional neural network (CNN). These methods are based on the well-known bag of words (BoW) model, which assumes documents are unordered collection of words. This assumption overlooks an important piece of information, i.e., word order. Moreover, the term frequency, which counts the number of occurrences of each word in SMS, is unable to distinguish the importance of words, due to the length limitation of SMS. This paper proposes a new method based on the discrete hidden Markov model (HMM) to use the word order information and to solve the low term frequency issue in SMS spam detection. The popularly adopted SMS spam dataset from the UCI machine learning repository is used for performance analysis of the proposed HMM method. The overall performance is compatible with deep learning by employing CNN and LSTM models. A Chinese SMS spam dataset with 2000 messages is used for further performance evaluation. Experiments show that the proposed HMM method is not language-sensitive and can identify spam with high accuracy on both datasets

Commercial Anti-Smishing Tools and Their Comparative Effectiveness Against Modern Threats

Smishing, also known as SMS phishing, is a type of fraudulent communication in which an attacker disguises SMS communications to deceive a target into providing their sensitive data. Smishing attacks use a variety of tactics; however, they have a similar goal of stealing money or personally identifying information (PII) from a victim. In response to these attacks, a wide variety of anti-smishing tools have been developed to block or filter these communications. Despite this, the number of phishing attacks continue to rise. In this paper, we developed a test bed for measuring the effectiveness of popular anti-smishing tools against fresh smishing attacks. To collect fresh smishing data, we introduce Smishtank.com, a collaborative online resource for reporting and collecting smishing data sets. The SMS messages were validated by a security expert and an in-depth qualitative analysis was performed on the collected messages to provide further insights. To compare tool effectiveness, we experimented with 20 smishing and benign messages across 3 key segments of the SMS messaging delivery ecosystem. Our results revealed significant room for improvement in all 3 areas against our smishing set. Most anti-phishing apps and bulk messaging services didn't filter smishing messages beyond the carrier blocking. The 2 apps that blocked the most smish also blocked 85-100\% of benign messages. Finally, while carriers did not block any benign messages, they were only able to reach a 25-35\% blocking rate for smishing messages. Our work provides insights into the performance of anti-smishing tools and the roles they play in the message blocking process. This paper would enable the research community and industry to be better informed on the current state of anti-smishing technology on the SMS platform